This is a major revision with much added functionality, listed roughly in order of importance. An additional vignette called relax
is supplied to describe the usage.
relax
argument added to glmnet
. This causes the models in the path to be refit without regularization. The resulting object inherits from class glmnet
, and has an additional component, itself a glmnet object, which is the relaxed fit.relax
argument to cv.glmnet
. This allows selection from a mixture of the relaxed fit and the regular fit. The mixture is governed by an argument gamma
with a default of 5 values between 0 and 1.predict
, coef
and plot
methods for relaxed
and cv.relaxed
objects.print
method for relaxed
object, and new print
methods for cv.glmnet
and cv.relaxed
objects.trace.it=TRUE
to glmnet
and cv.glmnet
. This can also be set for the session via glmnet.control
.assess.glmnet
, roc.glmnet
and confusion.glmnet
for displaying the performance of models.makeX
for building the x
matrix for input to glmnet
. Main functionality is one-hot-encoding of factor variables, treatment of NA
and creating sparse inputs.bigGlm
for fitting the GLMs of glmnet
unpenalized.In addition to these new features, some of the code in glmnet
has been tidied up, especially related to CV.
coxnet.deviance
to do with input pred
, as well as saturated loglike
(missing) and weightscoxgrad
function for computing the gradientinst/mortran
inst/mortran
-Wall
warningsnewoffset
created problems all over - fixed theseexact=TRUE
calls to coef
and predict
. See help file for more detailsy
blows up elnet
; error trap includedlambda.interp
which was returning NaN
under degenerate circumstances.predict
and coef
with exact=TRUE
. The user is strongly encouraged to supply the original x
and y
values, as well as any other data such as weights that were used in the original fit.predict.glmnet
, predict.multnet
and predict.coxnet
, when s=
argument is used with a vector of values. It was not doing the matrix multiply correctlyglmnet.control
for setting systems parameterscoxnet
mgaussian
family for multivariate responsegrouped
option for multinomial familynewx
and make dgCmatrix
if sparselognet
added a classnames component to the objectpredict.lognet(type="class")
now returns a character vector/matrixpredict.glmnet
: fixed bug with type="nonzero"
glmnet
: Now x can inherit from sparseMatrix
rather than the very specific dgCMatrix
, and this will trigger sparse mode for glmnetglmnet.Rd
(lambda.min
) : changed value to 0.01 if nobs < nvars
, (lambda
) added warnings to avoid single value, (lambda.min
): renamed it lambda.min.ratio
glmnet
(lambda.min
) : changed value to 0.01 if nobs < nvars
(HessianExact
) : changed the sense (it was wrong), (lambda.min
): renamed it lambda.min.ratio
. This allows it to be called lambda.min
in a call thoughpredict.cv.glmnet
(new function) : makes predictions directly from the saved glmnet
object on the cv objectcoef.cv.glmnet
(new function) : as abovepredict.cv.glmnet.Rd
: help functions for the abovecv.glmnet
: insert drop(y)
to avoid 1 column matrices; now include a glmnet.fit
object for later predictionsnonzeroCoef
: added a special case for a single variable in x
; it was dying on thisdeviance.glmnet
: includeddeviance.glmnet.Rd
: included